LRWR: Large-Scale Benchmark for Lip Reading in Russian language
This addresses the problem of limited language diversity in lipreading research for the field of visual speech recognition, though it is incremental as it extends existing methods to a new language.
The authors tackled the lack of lipreading datasets for non-English/Chinese languages by introducing LRWR, a large-scale benchmark for Russian with 235 classes and 135 speakers, and achieved new state-of-the-art results on the LRW benchmark.
Lipreading, also known as visual speech recognition, aims to identify the speech content from videos by analyzing the visual deformations of lips and nearby areas. One of the significant obstacles for research in this field is the lack of proper datasets for a wide variety of languages: so far, these methods have been focused only on English or Chinese. In this paper, we introduce a naturally distributed large-scale benchmark for lipreading in Russian language, named LRWR, which contains 235 classes and 135 speakers. We provide a detailed description of the dataset collection pipeline and dataset statistics. We also present a comprehensive comparison of the current popular lipreading methods on LRWR and conduct a detailed analysis of their performance. The results demonstrate the differences between the benchmarked languages and provide several promising directions for lipreading models finetuning. Thanks to our findings, we also achieved new state-of-the-art results on the LRW benchmark.